A data warehouse is a database designed to support decision-making in an organization. A typical data warehouse is batch updated on a periodic basis and contains an enormous amount of data. For example, large retail organizations may store one hundred gigabytes or more of transaction history in a data warehouse. In another example, tracking the usage of different resources or properties (e.g., web sites) across a network involves logging billions of hits every day and storing them in the data warehouse. The data in a data warehouse is typically historical and static and may also contain numerous summaries. It is structured to support a variety of analyses, including elaborate queries on large amounts of data that can require extensive searching.
Existing systems require creation of a new service to log information to different files within the data warehouse. Further, existing systems are restrictive in that they fail to allow customization of the files including customization of the columns in the files. Such systems also limit the log cut frequency to hourly and daily values.
A data warehouse may store user navigation data (e.g., clickstream data). Existing systems track the user navigation patterns by generating cookies and storing relevant information on a client device to be later stored in the data warehouse. Further, the existing systems require creation of a new service to track specific properties (e.g., web pages) or require modification to the properties to be tracked. For example, in existing systems, the individual properties read and write cookies that are stored on the client device. However, such de-centralized systems have to update each of the property pages every time a change in cookie behavior is desired.
Accordingly, a configurable logging system that filters, organizes, and stores a large amount of data is desired to address one or more of these and other disadvantages.